Marketing to potential customers is a crucial element of every business, and using available data into your marketing plans is an essential part of the process.
In this project, I tried to analyze and make a model to predict the customers' event for the offer and whether they will receive it, view it, make a transaction or complete it.
First I explored the data and see what I have to change before start the analysis.
Then I did some exploratory analysis on the data after cleaning.
From that analysis I found out that most favorite type of offers are Buy One Get One (BOGO) offers and Discount offers.
here you can find a blog post about this project : https://medium.com/@ArwaData/starbucks-capstone-challenge-30ef159a5b35
We'll work on the Starbucks's Dataset, which mimics how consumers make purchasing decisions and how promotional offers influence those decisions.
Buy-one-get-one (BOGO), discount, and informational offers are the three sorts of offers that can be sent.
We'll split client data based on several criteria and examine their behavior in response to various offers.
In the Data Analysis section of this project, we'll examine the data and try to answer the following questions:
- what is the percentages of all age ranges?
- How is the subscription date increasing over the years?
- what is the percentages of each gender?
- correlation heat map for profile dataset
- how does the age ranges effect the income of the starbucks customers?
- age distribution and gender in starbucks customers.
- what is the average age of starbucks customers?
- what is the average income of starbucks customers?
- what is the number of users for each year?
- what is the most frequent offer type?
- what is the most popular Offer Event to each Gender?
python (=>3.6)
pandas
numpy
Scikit-learn
sklearn
matplotlib
seaborn
- Udacity for providing an amazing Data Science Nanodegree Program
- Starbucks for providing the relevant dataset to train the model